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List regularization methods and trade-offs

GoogleMachine Learning Engineer · Technical Screen
HardMachine Learning EngineerTechnical ScreenMachine Learning

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This question evaluates competency in machine learning regularization and model generalization, covering familiarity with methods such as L1/L2, dropout, early stopping, data augmentation, label smoothing, mixup, normalization, Bayesian priors, and ensembling.

Which regularization techniques do you know and when would you use each? Compare L1 vs L2/weight decay, dropout, early stopping, data augmentation, label smoothing, mixup, normalization as an implicit regularizer, Bayesian priors, and ensembling. Explain their effects on bias–variance, sparsity, and training dynamics, and typical pitfalls.